Over 78 million American adults, or about one third of the population over 20 years old, suffer from hypertension, with an even higher prevalence in priority populations such as African American (AA) adults at 44%. Left untreated, hypertension can lead to a range of serious and costly health concerns, such as cardiovascular disease, stroke, and renal disease. While 80% of adults with hypertension are aware of their condition, less than half have their blood pressure (BP) under control. Among the many factors associated with a lack of BP control, patients' non-adherence to prescribed antihypertensive medications is a major concern. While little doubt exists that patients who more poorly adhere to their prescribed antihypertensive medication are at risk for worse BP outcomes, it is less clear how to accurately measure the impact of time- varying patient adherence on BP levels, and how to make predictions of BP trajectories that would be helpful for clinical-decision making. This proposed project is designed to address these questions. In this study, we will develop a novel approach to measuring BP control based on daily measurements of adherence through Bayesian dynamic linear models (DLM), and apply the approach to a pre-existing cohort of hypertensive patients. DLMs have a long history as a statistical framework for forecasting and measuring trajectories in many contexts, including real-time missile tracking as well as financial securities forecasting. The application of DLMs to measuring BP control is novel, but fits naturally into the DLM framework because BP can be tracked over time as adherence data is accumulated. The aims of our study include (1) to develop a Bayesian dynamic linear model framework for predicting BP levels from daily antihypertensive medication adherence, and assess the fit and applicability of the models, (2) to apply the DLM to measure the effects of different socio-demographic patient characteristics including race, different comorbid conditions, on BP levels, controlling for detailed adherence data, and (3) to develop an updating algorithm from the DLM that predicts credible ranges of future BP as a function of anticipated medication adherence, given current BP levels and covariate information. The results of the third aim, in particular, would be invaluable to clinicians and patients through a web-based or smart phone app as a means to monitor whether a patient's BP levels were too high relative to what might be expected based on good medication adherence, and would therefore be potentially useful as a clinical-decision making tool.